Statistical estimation for the scale parameter of the gamma distribution based on grouped data

This paper discusses point estimation for the scale parameter, λ, of gamma distribution based on grouped data. The existence and uniqueness of the MLE of λ are examined. It is shown that the MLE of λ is strongly consistent and is asymptotically unbiased.     

EXACT INFERENCE FOR FAMILY DISEASE CLUSTERS

In many epidemiologic studies the first indication of an environmental or genetic contribution to the risk of disease is the way in which the diseased cases cluster within the same family units. The concept of clustering is contrasted with incidence. We assume that all individuals within the same family are independent, up to their disease status. This assumption is used to provide an exact test of the initial hypothesis of no familial link with the disease, conditional on the number of diseased cases and the sizes of the various family units. Ascertainment bias is described and the appropriate sampling distribution is demonstrated. Two numerical examples with published data illustrate these methods.     

Über die asymptotische verteilung des periodogramms stationärer gaussscher zufälliger folgen

For stationary Gaussian random sequences it is estimated the rate of convergence to the asymptotic distribution of the periodogram. An asymptotic expansio for the distribution function of the periodogram is given as well.     

The split sample permutation t-tests

Without the exchangeability assumption, permutation tests for comparing two population means do not provide exact control of the probability of making a Type I error. Another drawback of permutation tests is that it cannot be used to test hypothesis about one population. In this paper, we propose a new type of permutation tests for testing the difference between two population means: the split sample permutation t-tests. We show that the split sample permutation t-tests do not require the exchangeability assumption, are asymptotically exact and can be easily extended to testing hypothesis about one population. Extensive simulations were carried out to evaluate the performance of two specific split sample permutation t-tests: the split in the middle permutation t-test and the split in the end permutation t-test. The simulation results show that the split in the middle permutation t-test has comparable performance to the permutation test if the population distributions are symmetric and satisfy the exchangeability assumption. Otherwise, the split in the end permutation t-test has significantly more accurate control of level of significance than the split in the middle permutation t-test and other existing permutation tests.     

The unbiasedness of hodges and lehman's test for a composite hypothesis of the man of a distribution with unknown variance

In 1954 Hodges and Lehmann considered the following problem: given is an i.i. normally distributed random sample with variance unknown. Under the null-hypothesis the mean is contained in a prescribed interval. Hodges and Lehmann constructed a test similar on the interval. This test is superior in power to the usual auxiliary procedure applied to this problem. Numerical calculations by Hodges and Lehmann indicated that the test is unbaised, however an analytical proof could not be given. In a recent paper the author proved unbiasedness for levels not too large, the magnitude depending on the sample size. Here the Proof is completed by establishing unbiasedness for all levels.     

On certain unbiased product estimators

When data on an auxiliary variate is available on all the units of the population, negatively correlated with the study variate, Robson (1957) and Murthy (1964) proposed product method of estimation for the estimation of the population total (mean) of the study variate. In this paper, we discuss a method given in Rao (1983) and obtain a simpler dervation of the class of unbiased product estimators for the case of Simple Random Sampling WithOut Replacement design as well as for the case of interpenetrating subsamples design which follows as a limiting case. Finally, we shall illustrate the results by means of two simple numerical example from live data.     

Bayesian estimation for time-series regressions improved with exact likelihoods

We propose an estimation procedure for time-series regression models under the Bayesian inference framework. With the exact method of Wise [Wise, J. (1955). The autocorrelation function and spectral density function. Biometrika, 42, 151–159], an exact likelihood function can be obtained instead of the likelihood conditional on initial observations. The constraints on the parameter space arising from the stationarity conditions are handled by a reparametrization, which was not taken into consideration by Chib [Chib, S. (1993). Bayes regression with autoregressive errors: A Gibbs sampling approach. J. Econometrics, 58, 275–294] or Chib and Greenberg [Chib, S. and Greenberg, E. (1994). Bayes inference in regression model with ARMA(p, q) errors. J. Econometrics, 64, 183–206]. Simulation studies show that our method leads to better inferential results than their results.     

Hilbert空间中框架的几个性质

本文研究了Hilbert空间中框架的几个性质,并订正了文献1中的一个错误结论。     

Improved WLP and GWP lower bounds based on exact integer programming

By using exact integer programming (IP) (integer programming in infinite precision) bounds on the word-length patterns (WLPs) and generalized word-length patterns (GWPs) for fractional factorial designs are improved. In the literature, bounds on WLPs are formulated as linear programming (LP) problems. Although the solutions to such problems must be integral, the optimization is performed without the integrality constraints. Two examples of this approach are bounds on the number of words of length four for resolution IV regular designs, and a lower bound for the GWP of two-level orthogonal arrays. We reformulate these optimization problems as IP problems with additional valid constraints in the literature and improve the bounds in many cases. We compare the improved bound to the enumeration results in the literature to find many cases for which our bounds are achieved. By using the constraints in our integer programs we prove that f(16λ,2,4)?9$f(16\lambda ,2,4)?9$ if λ$\lambda$ is odd where f(2^tλ,2,t)$f(2^t\lambda ,2,t)$ is the maximum n   for which an OA(N,n,2,t)$\mathrm{OA}(N,n,2,t)$ exists. We also present a theorem for constructing GMA OA(N,N/2-u,2,3)$\mathrm{OA}(N,N/2-u,2,3)$ for u=1,…,5$u=1,\dots ,5$.